{
  "id": 18292,
  "url": "https://arxiv.org/abs/2608.07862v1",
  "title": "SurakshaEval: An Indic Safety Benchmark for Multilingual LLMs",
  "summary": "Existing safety evaluation datasets for large language models (LLMs) predominantly focus on English and Western contexts, often overlooking the linguistic diversity and culturally grounded safety risks present in other languages. To address this gap, we introduce SurakshaEval, a novel safety benchmark composed of human-written prompts spanning real-world scenarios, explicitly designed for ten major Indian languages - Assamese, Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi, Punjabi, Tamil",
  "authors": "Debopriyo Banerjee, Kapil Rajesh Kavitha, Angana Borah, Xudong Han, Yuxia Wang, Parameswari Krishnamurthy, Utkarsh Agarwal, Atharva Kulkarni, Swaran Lata, Ayush Munot, Dhruv Sahnan, Aaryamonvikram Singh, Preslav Nakov, Monojit Choudhury",
  "category": "research",
  "topics": null,
  "orgs": null,
  "regions": "india",
  "published_at": "2026-08-08T02:03:33.000Z",
  "fetched_at": "2026-08-11T05:10:37.351Z",
  "source_slug": "x-arxiv-cs-cl-ethics-relevant-nlp",
  "source_name": "arXiv cs.CL (ethics-relevant NLP)",
  "source_homepage": "https://arxiv.org/list/cs.CL/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/18292",
  "original_url": "https://arxiv.org/abs/2608.07862v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}